Some personalized searches involve analyzing the user characteristics against a corpus of possible results to find the best options for a user. For example, an online content search may generate different results for different users depending on their background, education, experience, etc. Sometimes, users' actions online are considered during the selection of what online content to display to the users.
However, the number of users of an online service may be in the millions, and the categories of data associated with the users (e.g., educational institutions, current jobs, online content, etc.) may also be into the thousands or millions. Identifying online content that satisfies the interests of content consuming users and of creating users may be a computationally expensive proposition given the large amount of data and possible categories, thereby resulting in a technical problem of excessive consumption of the electronic resources of a computer system performing the search.